plot_emotion_matrix.py

# -*- coding: utf-8 -*-
import cv2
import sys
from constants import *
from emotion_recognition import EmotionRecognition
from os.path import join
import numpy as np
import matplotlib.pyplot as plt

# Load Model
network = EmotionRecognition()
network.build_network()

images = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_IMAGES_FILENAME))
labels = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_LABELS_FILENAME))
images = images.reshape([-1, SIZE_FACE, SIZE_FACE, 1])
labels = labels.reshape([-1, len(EMOTIONS)])

print '[+] Loading Data'
data = np.zeros((len(EMOTIONS),len(EMOTIONS)))
for i in xrange(images.shape[0]):
	result = network.predict(images[i])
	data[np.argmax(labels[i]), result[0].index(max(result[0]))] += 1
	#print x[i], ' vs ', y[i]

# Take % by column
for i in range(len(data)):
	total = np.sum(data[i])
	for x in range(len(data[0])):
		data[i][x] = data[i][x] / total
print data

print '[+] Generating graph'
c = plt.pcolor(data, edgecolors = 'k', linewidths = 4, cmap = 'Blues', vmin = 0.0, vmax = 1.0)
 
def show_values(pc, fmt="%.2f", **kw):
    from itertools import izip
    pc.update_scalarmappable()
    ax = pc.get_axes()
    ax.set_yticks(np.arange(len(EMOTIONS)) + 0.5, minor = False)
    ax.set_xticks(np.arange(len(EMOTIONS)) + 0.5, minor = False)
    ax.set_xticklabels(EMOTIONS, minor = False)
    ax.set_yticklabels(EMOTIONS, minor = False)
    for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
        x, y = p.vertices[:-2, :].mean(0)
        if np.all(color[:3] > 0.5):
            color = (0.0, 0.0, 0.0)
        else:
            color = (1.0, 1.0, 1.0)
        ax.text(x, y, fmt % value, ha = "center", va = "center", color = color, **kw)

show_values(c)
plt.xlabel('Predicted Emotion')
plt.ylabel('Real Emotion')
plt.show()